Mapping and analyzing the human liver proteome: progress and potential

Multi-Omic Approaches to Identify Genetic Factors in Metabolic Syndrome

Metabolic syndrome (MetS) is a highly heritable disease and a major public health burden worldwide. MetS diagnosis criteria are met by the simultaneous presence of any three of the following: high triglycerides, low HDL/high LDL cholesterol, insulin resistance, hypertension, and central obesity. These diseases act synergistically in people suffering from MetS and dramatically increase risk of morbidity and mortality due to stroke and cardiovascular disease, as well as certain cancers. Each of these component features is itself a complex disease, as is MetS. As a genetically complex disease, genetic risk factors for MetS are numerous, but not very powerful individually, often requiring specific environmental stressors for the disease to manifest. When taken together, all sequence variants that contribute to MetS disease risk explain only a fraction of the heritable variance, suggesting additional, novel loci have yet to be discovered. In this article, we will give a brief overview on the genetic concepts needed to interpret genome-wide association studies (GWAS) and quantitative trait locus (QTL) data, summarize the state of the field of MetS physiological genomics, and to introduce tools and resources that can be used by the physiologist to integrate genomics into their own research on MetS and any of its component features. There is a wealth of phenotypic and molecular data in animal models and humans that can be leveraged as outlined in this article. Integrating these multi-omic QTL data for complex diseases such as MetS provides a means to unravel the pathways and mechanisms leading to complex disease and promise for novel treatments. © 2022 American Physiological Society. Compr Physiol 12:1-40, 2022.

Non-targeted proteomics of acute respiratory distress syndrome: clinical and research applications

Acute respiratory distress syndrome (ARDS) is characterized by refractory hypoxemia caused by accumulation of pulmonary fluid with a high mortality rate, but the underlying mechanism is not yet fully understood, causing absent specific therapeutic drugs to treat with ARDS. In recent years, more and more studies have applied proteomics to ARDS. Non-targeted studies of proteomics in ARDS are just beginning and have the potential to identify novel drug targets and key pathways in this disease. This paper will provide a brief review of the recent advances in the application of non-targeted proteomics to ARDS.

Kinetic modelling of quantitative proteome data predicts metabolic reprogramming of liver cancer

Background

Metabolic alterations can serve as targets for diagnosis and cancer therapy. Due to the highly complex regulation of cellular metabolism, definite identification of metabolic pathway alterations remains challenging and requires sophisticated experimentation.

Methods

We applied a comprehensive kinetic model of the central carbon metabolism (CCM) to characterise metabolic reprogramming in murine liver cancer.

Results

We show that relative differences of protein abundances of metabolic enzymes obtained by mass spectrometry can be used to assess their maximal velocity values. Model simulations predicted tumour-specific alterations of various components of the CCM, a selected number of which were subsequently verified by in vitro and in vivo experiments. Furthermore, we demonstrate the ability of the kinetic model to identify metabolic pathways whose inhibition results in selective tumour cell killing.

Conclusions

Our systems biology approach establishes that combining cellular experimentation with computer simulations of physiology-based metabolic models enables a comprehensive understanding of deregulated energetics in cancer. We propose that modelling proteomics data from human HCC with our approach will enable an individualised metabolic profiling of tumours and predictions of the efficacy of drug therapies targeting specific metabolic pathways.

The "Missing" Proteome: Undetected Proteins, Not-Translated Transcripts, and Untranscribed Genes

The Chromosome-centric Human Proteome Project aims at characterizing the expression of proteins encoded in each chromosome at the tissue, cell, and subcellular levels. The proteomic profiling of a particular tissue or cell line commonly results in a substantial portion of proteins that are not observed (the "missing" proteome). The concurrent transcriptome profiling of the analyzed tissue/cells samples may help define the set of untranscribed genes in a given type of tissue or cell, thus narrowing the size of the "missing" proteome and allowing us to focus on defining the reasons behind undetected proteins, namely, whether they are technical (insufficient sensitivity of protein detection) or biological (correspond to not-translated transcripts). We believe that the quantitative polymerase chain reaction (qPCR) can provide an efficient approach to studying low-abundant transcripts related to undetected proteins due to its high sensitivity and the possibility of ensuring the specificity of detection via the simple Sanger sequencing of PCR products. Here we illustrated the feasibility of such an approach on a set of low-abundant transcripts. Although inapplicable to the analysis of whole transcriptome, qPCR can successfully be utilized to profile a limited cohort of transcripts encoded on a particular chromosome, as we previously demonstrated for human chromosome 18.

Utilization of dried and long-term stored polyacrylamide gels for the advanced proteomic profiling of mitochondrial contact sites from rat liver

Following subcellular fractionation, the complexity of proteins derived from a particular cellular compartment is often evaluated by gel electrophoretic analysis. For the proteomic cataloguing of these distinct protein populations and their biochemical characterization, gel electrophoretic protein separation can be conveniently combined with liquid chromatography mass spectrometry. Here we describe a gel-enhanced liquid chromatography mass spectrometry (GeLC-MS)/MS approach with a new bioanalytical focus on the proteomic profiling of mitochondrial contact sites from rat liver using the highly sensitive Orbitrap Fusion Tribrid mass spectrometer for optimum protein identification following extraction from dried and long-term stored gels. Mass spectrometric analysis identified 964 protein species in the mitochondrial contact site fraction, whereby 459 proteins were identified by ≥3 unique peptides. This included mitochondrial components of the supramolecular complexes that form the ATP synthase, the respiratory chain, ribosomal subunits and the cytochrome P450 system, as well as crucial components of the translocase complexes translocase of the inner membrane (TIM) and translocase of the outer membrane (TOM) of the two mitochondrial membranes. Proteomics also identified contact site markers, such as glutathione transferase, monoamine oxidase and the pore protein voltage dependent anion channel (VDAC)-1. Hence, this report demonstrates that the GeLC-MS/MS method can be used to study complex mixtures of proteins that have been embedded and stored in dried polyacrylamide gels for a long period of time. Careful re-swelling and standard in-gel digestion is suitable to produce peptide profiles from old gels that can be used to extract sophisticated proteomic maps and enable the subsequent bioinformatics analysis of the distribution of protein function and the determination of potential protein clustering within the contact site system.

AAgAtlas 1.0: a human autoantigen database

Autoantibodies refer to antibodies that target self-antigens, which can play pivotal roles in maintaining homeostasis, distinguishing normal from tumor tissue and trigger autoimmune diseases. In the last three decades, tremendous efforts have been devoted to elucidate the generation, evolution and functions of autoantibodies, as well as their target autoantigens. However, reports of these countless previously identified autoantigens are randomly dispersed in the literature. Here, we constructed an AAgAtlas database 1.0 using text-mining and manual curation. We extracted 45 830 autoantigen-related abstracts and 94 313 sentences from PubMed using the keywords of either ‘autoantigen’ or ‘autoantibody’ or their lexical variants, which were further refined to 25 520 abstracts, 43 253 sentences and 3984 candidates by our bio-entity recognizer based on the Protein Ontology. Finally, we identified 1126 genes as human autoantigens and 1071 related human diseases, with which we constructed a human autoantigen database (AAgAtlas database 1.0). The database provides a user-friendly interface to conveniently browse, retrieve and download human autoantigens as well as their associated diseases. The database is freely accessible at http://biokb.ncpsb.org/aagatlas/. We believe this database will be a valuable resource to track and understand human autoantigens as well as to investigate their functions in basic and translational research.